Because of the problems of low allocation efficiency and accuracy in the traditional resource allocation model for the intelligent construction of track and field team training venues, this paper proposes a new intelligent construction resource allocation model for track and field team training venues. A distributed data collection method is adopted to select the index data for the intelligent construction of the track and field team training venues to optimize the allocation of resources and the mined resource allocation index through extracting the optimized index characteristics. The goal of the optimization problem is transformed into a single-objective solving problem. The suggested approach is based on the two-level programming concept. The simulation experimental results show higher efficiency and better allocation effect of the proposed scheme compared to other state-of-the-art approaches. Our evaluation and observations suggest that the accuracy of the intelligent construction of track and field team training venues is between 90% and 99% which is higher than two other benchmarked models. Furthermore, the proposed model can quickly reach to an optimal decision.

中文翻译：

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田径队训练场地智能建设资源优化配置

针对传统田径队训练场馆智能建设资源分配模型存在分配效率和准确率不高的问题，本文提出了一种新的田径队训练场智能建设资源分配模型。采用分布式数据采集方法，选取指标数据进行田径队训练场地智能建设，通过提取优化后的指标特征，优化资源配置和挖掘资源配置指标。优化问题的目标转化为单目标求解问题。建议的方法基于两级编程概念。仿真实验结果表明，与其他最先进的方法相比，所提出的方案具有更高的效率和更好的分配效果。我们的评估和观察表明，田径队训练场馆智能化建设的准确率在90%到99%之间，高于其他两个基准模型。此外，所提出的模型可以快速达到最佳决策。